Stochastic uncertainty-based optimisation on an aerogel glazing building in China using supervised learning surrogate model and a heuristic optimisation algorithm

Scenario parameters of aerogel glazing systems are with uncertainties in the real operation, whereas current literature fails to characterise the thermal and energy responses regarding stochastic scenario uncertainties. Furthermore, multi-level uncertainty-based optimisation has been rarely studied for the robustness improvement. In this study, a general method for stochastic uncertainties-based optimisation is proposed. A machine-learning based surrogate model is developed for uncertainty analysis. Furthermore, a multi-level uncertainty-based optimisation function is characterized and integrated with the heuristic teaching-learning-based algorithm to search for the optimal design. Research results indicated that, in the multi-level uncertainty-based optimal scenario, average values of RoC, thickness of aerogel layer, extinction coefficient and thermal conductivity are 306253.4 J/(K m³), 24.5 mm, 0.092, and 0.0214 W/(m K). Compared to the deterministic case, the stochastic uncertainty case can decrease the heat flux from 237.16 to 190 kWh/m².a by 19.9%, and total heat gain from 267.18 to 222.04 kWh/m².a by 16.9%. Furthermore, by adopting the multi-level uncertainty-based optimisation, the heat flux can be further reduced to 162.54 kWh/m².a by 31.5%, and the total heat gain to 191.56 kWh/m².a by 28.3%. The proposed technique can improve the reliability of aerogel glazing systems in green buildings.